Kang, Jian und Körner, Marco und Wang, Yuanyuan und Taubenböck, Hannes und Zhu, Xiao Xiang (2018) Building instance classification using street view images. ISPRS Journal of Photogrammetry and Remote Sensing. Elsevier. doi: 10.1016/j.isprsjprs.2018.02.006. ISSN 0924-2716. (im Druck)
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Offizielle URL: https://www.sciencedirect.com/science/article/pii/S0924271618300352
Kurzfassung
Land-use classification based on spaceborne or aerial remote sensing images has been extensively studied over the past decades. Such classification is usually a patch-wise or pixel-wise labeling over the whole image. But for many applications, such as urban population density mapping or urban utility planning, a classification map based on individual buildings is much more informative. However, such semantic classification still poses some fundamental challenges, for example, how to retrieve fine boundaries of individual buildings. In this paper, we proposed a general framework for classifying the functionality of individual buildings. The proposed method is based on Convolutional Neural Networks (CNNs) which classify façade structures from street view images, such as Google StreetView, in addition to remote sensing images which usually only show roof structures. Geographic information was utilized to mask out individual buildings, and to associate the corresponding street view images. We created a benchmark dataset which was used for training and evaluating CNNs. In addition, the method was applied to generate building classification maps on both region and city scales of several cities in Canada and the US.
elib-URL des Eintrags: | https://elib.dlr.de/120030/ | ||||||||||||||||||||||||
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Dokumentart: | Zeitschriftenbeitrag | ||||||||||||||||||||||||
Titel: | Building instance classification using street view images | ||||||||||||||||||||||||
Autoren: |
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Datum: | 2 März 2018 | ||||||||||||||||||||||||
Erschienen in: | ISPRS Journal of Photogrammetry and Remote Sensing | ||||||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||||||
Open Access: | Ja | ||||||||||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||||||||||
In ISI Web of Science: | Ja | ||||||||||||||||||||||||
DOI: | 10.1016/j.isprsjprs.2018.02.006 | ||||||||||||||||||||||||
Verlag: | Elsevier | ||||||||||||||||||||||||
ISSN: | 0924-2716 | ||||||||||||||||||||||||
Status: | im Druck | ||||||||||||||||||||||||
Stichwörter: | CNN, individual building classification, building instance, street view image, deep learning, remote sensing, data fusion | ||||||||||||||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||||||||||
HGF - Programm: | Raumfahrt | ||||||||||||||||||||||||
HGF - Programmthema: | Erdbeobachtung | ||||||||||||||||||||||||
DLR - Schwerpunkt: | Raumfahrt | ||||||||||||||||||||||||
DLR - Forschungsgebiet: | R EO - Erdbeobachtung | ||||||||||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | R - Vorhaben hochauflösende Fernerkundungsverfahren (alt) | ||||||||||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||||||||||
Institute & Einrichtungen: | Institut für Methodik der Fernerkundung > EO Data Science Deutsches Fernerkundungsdatenzentrum > Georisiken und zivile Sicherheit | ||||||||||||||||||||||||
Hinterlegt von: | Wang, Yuanyuan | ||||||||||||||||||||||||
Hinterlegt am: | 24 Mai 2018 13:55 | ||||||||||||||||||||||||
Letzte Änderung: | 20 Jun 2021 15:51 |
Verfügbare Versionen dieses Eintrags
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Building instance classification using street view images. (deposited 13 Okt 2017 11:16)
- Building instance classification using street view images. (deposited 24 Mai 2018 13:55) [Gegenwärtig angezeigt]
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